Object tracking using edge detection

This paper is concerned with an algorithm of determining a position of an object and its borders in an X-Ray image. The algorithm is based on a preliminary estimation of a histogram of given image. The information retrieved from estimation provides complementary parameters for further edge detection. As a result, this approach allows reducing the processing time. The future research and development of the algorithm will be aimed at object tracking in real-time systems.

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More Robust Object Tracking via Semi-supervised Metric Learning and Edge Detection

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac.2018.8577616 ◽

2018 ◽

Author(s):

Liujun Zhao ◽

Weizheng Kong ◽

Ming Qin

Keyword(s):

Edge Detection ◽

Object Tracking ◽

Metric Learning

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UnCanny: Exploiting Reversed Edge Detection as a Basis for Object Tracking in Video

Journal of Imaging ◽

10.3390/jimaging7050077 ◽

2021 ◽

Vol 7 (5) ◽

pp. 77

Author(s):

Wesley T. Honeycutt ◽

Eli S. Bridge

Keyword(s):

Edge Detection ◽

Object Detection ◽

Object Tracking ◽

False Positive ◽

Detection Methods ◽

Video Footage ◽

Computational Expense ◽

Gaussian Blurring ◽

Sobel Edge Detection ◽

Method Show

Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang–Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal “false positive” noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods.

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Difference mode EXELFS with parallel-detection EELS

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100130948 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1262-1263

Author(s):

Michael K. Kundmann ◽

Ondrej L. Krivanek

Keyword(s):

Edge Detection ◽

Important Distinction ◽

Edge Structure ◽

Parallel Detection ◽

Gain Variation ◽

Ideal Solutions ◽

Detector Cell ◽

The Difference ◽

Elemental Detection ◽

Partial Correction

Parallel detection has greatly improved the elemental detection sensitivities attainable with EELS. An important element of this advance has been the development of differencing techniques which circumvent limitations imposed by the channel-to-channel gain variation of parallel detectors. The gain variation problem is particularly severe for detection of the subtle post-threshold structure comprising the EXELFS signal. Although correction techniques such as gain averaging or normalization can yield useful EXELFS signals, these are not ideal solutions. The former is a partial throwback to serial detection and the latter can only achieve partial correction because of detector cell inhomogeneities. We consider here the feasibility of using the difference method to efficiently and accurately measure the EXELFS signal.An important distinction between the edge-detection and EXELFS cases lies in the energy-space periodicities which comprise the two signals. Edge detection involves the near-edge structure and its well-defined, shortperiod (5-10 eV) oscillations. On the other hand, EXELFS has continuously changing long-period oscillations (∼10-100 eV).

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Multiple-Object Tracking

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000085 ◽

2011 ◽

Vol 58 (3) ◽

pp. 196-200 ◽

Cited By ~ 15

Author(s):

K. Botterill ◽

R. Allen ◽

P. McGeorge

Keyword(s):

Object Tracking ◽

Cognitive Processes ◽

Spatial Location ◽

Multiple Object Tracking ◽

Tracking Performance ◽

Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

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